Temporal Changes in the Content of Polyarenes in Samples of the Seasonally Thawed Layer from Tundra Peatlands during a Model Experiment

Yakovleva, E. V.; Габов, Д. Н.

doi:10.1134/s1062359022050211

Cited by 1 publication

(1 citation statement)

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“…The main objective of time series forecasting is to identify patterns and trends in historical data and utilize them to make accurate predictions for the future. In order to achieve accurate forecasts, conventional time series forecasting models typically incorporate autoregressive moving average (ARMA) model [3], seasonal autoregressive moving average (SARMA) model [4] and seasonal and trend decomposition using loess (STL) model [5], and so forth. However, traditional time series forecasting methods are limited to predicting short-term trends and are not as effective for longterm forecasting.…”

Section: Introductionmentioning

confidence: 99%

A granularity time series forecasting model combining three-way decision and trend information granularity

Qiu,

Su,

Qian

2024

Preprint

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In recent decades, significant advancements have been made in the field of time series data mining, leading to its widespread application in various domains. However, the existence of data correlation in time series data sets brings challenges to long-term prediction. One approach to address this issue is to transform the original time series into granular time series (GTS). Therefore, a prediction model based on GTS is proposed to meet this challenge in this study. Firstly, the improved L1-trend filtering is used to achieve the optimal segmentation of information particles. Then, a three-way decisions (TWD) model based on shape similarity is used to compress and aggregate information granules. Finally, a GTS prediction model based on LSTM neural network is established. The model effectively retains the trend information of the time series and overcomes the limitation that the existing models cannot adjust the granularity length of the original information. In addition, the proposed model is applied to several real datasets for sensitivity analysis and comparative analysis. The results show that the model has strong performance in long-term forecasting.

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Section: Introductionmentioning

confidence: 99%